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Gijs de Boer
,
Brian J. Butterworth
,
Jack S. Elston
,
Adam Houston
,
Elizabeth Pillar-Little
,
Brian Argrow
,
Tyler M. Bell
,
Phillip Chilson
,
Christopher Choate
,
Brian R. Greene
,
Ashraful Islam
,
Ryan Martz
,
Michael Rhodes
,
Daniel Rico
,
Maciej Stachura
,
Francesca M. Lappin
,
Antonio R. Segales
,
Seabrooke Whyte
, and
Matthew Wilson

Abstract

Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

Open access
Nina Horat
and
Sebastian Lerch

Abstract

Subseasonal weather forecasts are becoming increasingly important for a range of socioeconomic activities. However, the predictive ability of physical weather models is very limited on these time scales. We propose four postprocessing methods based on convolutional neural networks to improve subseasonal forecasts by correcting systematic errors of numerical weather prediction models. Our postprocessing models operate directly on spatial input fields and are therefore able to retain spatial relationships and to generate spatially homogeneous predictions. They produce global probabilistic tercile forecasts for biweekly aggregates of temperature and precipitation for weeks 3–4 and 5–6. In a case study based on a public forecasting challenge organized by the World Meteorological Organization, our postprocessing models outperform the bias-corrected forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), and achieve improvements over climatological forecasts for all considered variables and lead times. We compare several model architectures and training modes and demonstrate that all approaches lead to skillful and well-calibrated probabilistic forecasts. The good calibration of the postprocessed forecasts emphasizes that our postprocessing models reliably quantify the forecast uncertainty based on deterministic input information in the form of ECMWF ensemble mean forecast fields only.

Open access
Ryosuke Okugawa
,
Kazuaki Yasunaga
,
Atsushi Hamada
, and
Satoru Yokoi

Abstract

Large amounts of tropical precipitation have been observed as significantly concentrated over the western coast of Sumatra Island. In the present study, we used a cloud-resolving model to perform 14-day numerical simulations and reproduce the distinctive precipitation distributions over western Sumatra Island and adjacent areas. The control experiment, in which the warmer sea surface temperature (SST) near the coast was incorporated and the terminal velocity and effective radius of ice clouds were parameterized to be temperature dependent, adequately reproduced the precipitation concentration as well as the diurnal cycles of precipitation. We then used the column-integrated frozen moist static energy budget equation, which is virtually equivalent to the column-integrated moisture budget equation under the weak temperature gradient assumption, to formulate sensitivity experiments focusing on the effects of coastal SST and upper-level ice clouds. Analysis of the time-averaged fields revealed that the column-integrated moisture and precipitation in the coast were significantly reduced when a cooler coastal SST or larger ice cloud particle size was assumed. Based on the comparison of the sensitivity experiments and in situ observations, we speculate that ice clouds, which are exported from inland convection that is strictly regulated by solar radiation, promote the accumulation of moisture in the coastal region by mitigating radiative cooling. Together with the moisture and heat supplied by the warm ocean surface, they contribute to the large amounts of precipitation here.

Open access
Azusa Takeishi
and
Chien Wang

Abstract

Raindrop formation processes in warm clouds mainly consist of condensation and collision–coalescence of small cloud droplets. Once raindrops form, they can continue growing through collection of cloud droplets and self-collection. In this study, we develop novel emulators to represent raindrop formation as a function of various physical or background environmental conditions by using a sophisticated aerosol–cloud model containing 300 droplet size bins and machine learning methods. The emulators are then implemented in two microphysics schemes in the Weather Research and Forecasting Model and tested in two idealized cases. The simulations of shallow convection with the emulators show a clear enhancement of raindrop formation compared to the original simulations, regardless of the scheme in which they were embedded. On the other hand, the simulations of deep convection show a more complex response to the implementation of the emulators, in terms of the changes in the amount of rainfall, due to the larger number of microphysical processes involved in the cloud system (i.e., ice-phase processes). Our results suggest the potential of emulators to replace the conventional parameterizations, which may allow us to improve the representation of physical processes at an affordable computational expense.

Significance Statement

Formation of raindrops marks a critical stage in cloud evolution. Accurate representations of raindrop formation processes require detailed calculations of cloud droplet growth processes. These calculations are often not affordable in weather and climate models as they are computationally expensive due to their complex dependence on cloud droplet size distributions and dynamical conditions. As a result, simplified parameterizations are more frequently used. In our study we trained machine learning models to learn raindrop formation rates from detailed calculations of cloud droplet evolutions in 1000 parcel-model simulations. The implementation of the developed models or the emulators in a weather forecasting model shows a change in the total rainfall and cloud characteristics, indicating the potential improvement of cloud representations in models if these emulators replace the conventional parameterizations.

Open access
Víctor C. Mayta
and
Ángel F. Adames Corraliza

Abstract

Observations of column water vapor in the tropics show significant variations in space and time, indicating that it is strongly influenced by the passage of weather systems. It is hypothesized that many of the influencing systems are moisture modes, systems whose thermodynamics are governed by moisture. On the basis of four objective criteria, results suggest that all oceanic convectively coupled tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These modes occur where the horizontal column moisture gradient is steep and not where the column water vapor content is high. Despite geographical basic-state differences, the moisture modes are driven by the same mechanisms across all basins. The moist static energy (MSE) anomalies propagate westward by horizontal moisture advection by the trade winds. Their growth is determined by the advection of background moisture by the anomalous meridional winds and anomalous radiative heating. Horizontal maps of column moisture and 850-hPa streamfunction show that convection is partially collocated with the low-level circulation in nearly all the waves. Both this structure and the process of growth indicate that the moisture modes grow from moisture–vortex instability. Last, space–time spectral analysis reveals that column moisture and low-level meridional winds are coherent and exhibit a phasing that is consistent with a poleward latent energy transport. Collectively, these results indicate that moisture modes are ubiquitous across the tropics. That they occur in regions of steep horizontal moisture gradients and grow from moisture–vortex instability suggests that these gradients are inherently unstable and are subject to continuous stirring.

Significance Statement

Over the tropics, column water vapor has been found to be highly correlated with precipitation, especially in slowly evolving systems. These observations and theory support the hypothesis that moisture modes exist, a type of precipitating weather system that does not exist in dry theory. In this study, we found that all oceanic tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These systems exist in regions where moisture varies greatly in space, and they grow by transporting air from the humid areas of the tropics toward their low pressure center. These results indicate that the climatological-mean distribution of moisture in the tropics is unstable and is subject to stirring by moisture modes.

Open access
Clara Deser
,
Adam S. Phillips
,
Michael. A. Alexander
,
Dillon J. Amaya
,
Antonietta Capotondi
,
Michael G. Jacox
, and
James D. Scott

Abstract

The future evolution of sea surface temperature (SST) extremes is of great concern, not only for the health of marine ecosystems and sustainability of commercial fisheries, but also for precipitation extremes fueled by moisture evaporated from the ocean. This study examines the projected influence of anthropogenic climate change on the intensity and duration of monthly SST extremes, hereafter termed marine heat waves (MHWs) and marine cold waves (MCWs), based on initial-condition large ensembles with seven Earth system models. The large number of simulations (30–100) with each model allows for robust quantification of future changes in both the mean state and variability in each model. In general, models indicate that future changes in variability will cause MHW and MCW events to intensify in the northern extratropics and weaken in the tropics and Southern Ocean, and to shorten in duration in many areas. These changes are generally symmetric between MHWs and MCWs, except for the longitude of duration change in the tropical Pacific and sign of duration change in the Arctic. Projected changes in ENSO account for a large fraction of the variability-induced changes in MHW and MCW characteristics in each model and are responsible for much of the intermodel spread as a result of differences in future ENSO behavior. The variability-related changes in MHW and MCW characteristics noted above are superimposed upon large mean-state changes. Indeed, their contribution to the total change in SST during MHW and MCW events is generally <10% except in polar regions where they contribute upward of 50%.

Open access
Weizhen Chen
,
Chang-Hoi Ho
,
Song Yang
,
Zeming Wu
, and
Hongjing Chen

Abstract

The Madden–Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO) are prominent components of the intraseasonal oscillations over the tropical Indo-Pacific Ocean. This study examines the tropical cyclone (TC) genesis over the Bay of Bengal (BOB) and the South China Sea (SCS) on an intraseasonal scale in May–June during 1979–2021. Results show that the convection associated with the two types of intraseasonal oscillations simultaneously modulates TC genesis in both ocean basins. As the MJO/QBWO convection propagated, TCs form alternately over the two basins, with a significant increase (decrease) in TC genesis frequency in the convective (nonconvective) MJO/QBWO phase. Based on the anomalous genesis potential index associated with the MJO/QBWO, an assessment of the influence of various factors on TC genesis is further assessed. Middle-level relative humidity and lower-level relative vorticity play key roles in the MJO/QBWO modulation on TC genesis. The MJO primarily enhances large-scale cross-equatorial moisture transport, resulting in significant moisture convergence, while the QBWO generally strengthens the monsoon trough and induces the retreat of the western North Pacific subtropical high, increasing the regional lower-level relative vorticity. The potential intensity and vertical wind shear make small or negative contributions. This study provides insights into the neighboring basin TC relationship at intraseasonal scales, which has a potential to improve the short-term prediction of regional TC activity.

Significance Statement

The Madden–Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO) are two types of intraseasonal tropical atmospheric oscillations. The development of tropical cyclones (TCs) is often accompanied by intraseasonal convection. This study highlights the distinct roles of MJO and QBWO in TC genesis over the South Asian marginal seas (e.g., Bay of Bengal and South China Sea). The QBWO can co-regulate TC genesis along with the background of the MJO, where the large-scale MJO mainly provides moisture, while the small-scale QBWO mainly contributes to vorticity. These findings provide useful information for subseasonal TCs forecasting. There are many developing countries along the South Asian marginal seacoast; therefore, further research on regional TC climate would help effectively reduce casualties and property damage.

Open access
Maya I. Jakes
,
Helen E. Phillips
,
Annie Foppert
,
Ajitha Cyriac
,
Nathaniel L. Bindoff
,
Stephen R. Rintoul
, and
Andrew F. Thompson

Abstract

Eddy stirring at mesoscale oceanic fronts generates finescale filaments, visible in submesoscale-resolving model simulations and high-resolution satellite images of sea surface temperature, ocean color, and sea ice. Submesoscale filaments have widths of O(1–10) km and evolve on time scales of hours to days, making them extremely challenging to observe. Despite their relatively small scale, submesoscale processes play a key role in the climate system by providing a route to dissipation; altering the stratification of the ocean interior; and generating strong vertical velocities that exchange heat, carbon, nutrients, and oxygen between the mixed layer and the ocean interior. We present a unique set of in situ and satellite observations in a standing meander region of the Antarctic Circumpolar Current (ACC) that supports the theory of cold filamentary intensification—revealing enhanced vertical velocities and evidence of subduction and ventilation associated with finescale cold filaments. We show that these processes are not confined to the mixed layer; EM-APEX floats reveal enhanced downward velocities (>100 m day−1) and evidence of ageostrophic motion extending as deep as 1600 dbar, associated with a ∼20-km-wide cold filament. A finer-scale (∼5 km wide) cold filament crossed by a towed Triaxus is associated with anomalous chlorophyll and oxygen values extending at least 100–200 dbar below the base of the mixed layer, implying recent subduction and ventilation. Energetic standing meanders within the weakly stratified ACC provide an environment conductive to the generation of finescale filaments that can transport water mass properties across mesoscale fronts and deep into the ocean interior.

Open access
Stephanie S. Rushley
,
Matthew A. Janiga
,
William Crawford
,
Carolyn A. Reynolds
,
William Komaromi
, and
Justin McLay

Abstract

Accurately simulating the Madden–Julian oscillation (MJO), which dominates intraseasonal (30–90 day) variability in the tropics, is critical to predicting tropical cyclones (TCs) and other phenomena at extended-range (2–3 week) time scales. MJO biases in intensity and propagation speed are a common problem in global coupled models. For example, the MJO in the Navy Earth System Prediction Capability (ESPC), a global coupled model, has been shown to be too strong and too fast, which has implications for the MJO–TC relationship in that model. The biases and extended-range prediction skill in the operational version of the Navy ESPC are compared to experiments applying different versions of analysis correction-based additive inflation (ACAI) to reduce model biases. ACAI is a method in which time-mean and stochastic perturbations based on analysis increments are added to the model tendencies with the goals of reducing systematic error and accounting for model uncertainty. Over the extended boreal summer (May–November), ACAI reduces the root-mean-squared error (RMSE) and improves the spread–skill relationship of the total tropical and MJO-filtered OLR and low-level zonal winds. While ACAI improves skill in the environmental fields of low-level absolute vorticity, potential intensity, and vertical wind shear, it degrades the skill in the relative humidity, which increases the positive bias in the genesis potential index (GPI) in the operational Navy ESPC. Northern Hemisphere integrated TC genesis biases are reduced (increased number of TCs) in the ACAI experiments, which is consistent with the positive GPI bias in the ACAI simulations.

Open access
Clayton R. S. Sasaki
,
Angela K. Rowe
,
Lynn A. McMurdie
,
Adam C. Varble
, and
Zhixiao Zhang

Abstract

This study documents the spatial and temporal distribution of the South American low-level jet (SALLJ) and quantifies its impact on the convective environment using a 6.5-month convection-permitting simulation during the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) campaigns. Overall, the simulation reproduces the observed SALLJ characteristics in central Argentina near the Sierras de Córdoba (SDC), a focal point for terrain-focused upscale growth. SALLJs most frequently occur in the summer with maxima to the northwest and east of the SDC and minima over the higher terrain. The shallower SALLJs (<1750 m) have a strong overnight skew, while the elevated jets are more equally spread throughout the day. SALLJ periods often have higher amounts of low-level moisture and instability compared to non-SALLJ periods, with these impacts increasing over time when the SALLJ is present and decreasing afterward. The SALLJ may enhance low-level wind shear magnitudes (particularly when accounting for the jet height); however, enhancement is somewhat limited due to the presence of speed shear in most situations. SALLJ periods are associated with low-level directional shear favorable for organized convection and an orientation of cloud-layer wind shear parallel to the terrain, which could favor upscale growth. A case study is shown in which the SALLJ influenced both the magnitude and direction of wind shear concurrent with convective upscale growth near the SDC. This study highlights the complex relationship between the SALLJ and its impacts during periods of widespread convection.

Significance Statement

Areas of enhanced low-level winds, or low-level jets, likely promote favorable conditions for upscale growth, the processes by which storms grow larger. Central Argentina is an ideal place to study the influence of low-level jets on upscale growth as storms often stay connected to the Sierras de Córdoba Mountain range, growing over a relatively small area. This study uses model data to describe the distribution and impact of the South American low-level jet on the storm environment. The South American low-level jet is frequently found near the Sierras de Córdoba, and moisture and convective instability increase when it is present. However, the jet’s impact on other conditions important for upscale growth, such as vertical wind shear, is not as straightforward.

Open access